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Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

Bannister, R. N., Migliorini, S., Rudd, A. C. and Baker, L. H. (2018) Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble. Geoscientific Model Development Discussions. 260. ISSN 1991-962X

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To link to this item DOI: 10.5194/gmd-2017-260


Ensemble-based predictions are increasingly used as an aid to weather forecasting and to data assimilation, where the aim is to capture the range of possible outcomes consistent with the underlying uncertainties. Constraints on computing resources mean that ensembles have a relatively small size, which can lead to an incomplete range of possible outcomes, and to inherent sampling errors. This paper discusses how an existing ensemble can be relatively easily increased in size, it develops a range of standard and extended diagnostics to help determine whether a given ensemble is ‘large enough’ to be useful for forecasting and data assimilation purposes, and it applies the diagnostics to a convective-scale case study for illustration. Diagnostics include the effect of ensemble size on various aspects of rainfall forecasts, kinetic energy spectra, and (co)variance statistics in the spatial and spectral domains. The work here extends the Met Office’s 24 ensemble members to 93. It is found that the extra members do develop a significant degree of linear independence, they increase the ensemble spread (although with caveats to do with non-Gaussianity), they reduce sampling error in many statistical quantities (namely variances, correlations, and length-scales), and improve the effective spatial resolution of the ensemble. The extra members though do not improve the probabilistic rain rate forecasts. It is assumed that the 93-member ensemble approximates the error-free statistics, which is a practical assumption, but the data suggests that this number of members is ultimately not enough to justify this assumption, and therefore more ensembles are likely required for such convective-scale systems to further reduce sampling errors, especially for ensemble data assimilation purposes.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO)
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:76001
Additional Information:This article has been peer reviewed and is available on-line in the discussions journal. It was not accepted for publication in the full journal despite the fact that we refuted the reviewers' comments that lead to this decision.
Publisher:European Geosciences Union


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